{
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  "metadata": {
    "colab": {
      "name": "FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
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    "pycharm": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL/blob/master/FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading Using Ensemble Strategy\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks using ensemble strategy in one Jupyter Notebook | Presented at ICAIF 2020\n",
        "\n",
        "* This notebook is the reimplementation of our paper: Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, using FinRL.\n",
        "* Check out medium blog for detailed explanations: https://medium.com/@ai4finance/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPT0ipYE28wL",
        "outputId": "e090b22c-8499-42f7-f4a1-49520631d11c"
      },
      "source": [
        "# ## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Running command git clone -q https://github.com/AI4Finance-Foundation/ElegantRL.git /tmp/pip-install-ezrt4uw9/elegantrl_55a3f3c3f82a4ab7924333e69dd21b06\n",
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            "  Stored in directory: /root/.cache/pip/wheels/2a/f5/49/9c0d851aa64b58db72883cf9393cc824d536bdf13f5c83cff4\n",
            "  Building wheel for gpustat (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gpustat: filename=gpustat-1.0.0b1-py3-none-any.whl size=15979 sha256=a125e11ecb6ea252334188ef2356e3cef9a94fef6d1c3b2ae7028a76777ff3b9\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/16/e2/3e2437fba4c4b6a97a97bd96fce5d14e66cff5c4966fb1cc8c\n",
            "Successfully built finrl elegantrl pyfolio empyrical exchange-calendars gputil thriftpy2 gpustat\n",
            "Installing collected packages: requests, multidict, frozenlist, yarl, lxml, deprecated, asynctest, async-timeout, aiosignal, redis, pyyaml, pycares, ply, platformdirs, opencensus-context, msgpack, hiredis, distlib, blessed, aiohttp, websockets, websocket-client, virtualenv, thriftpy2, tensorboardX, stable-baselines3, ray, pymysql, pyluach, pybullet, py-spy, psycopg2-binary, opencensus, nodeenv, mock, identify, gpustat, empyrical, cryptography, colorful, cfgv, box2d-py, aioredis, aiohttp-cors, aiodns, yfinance, wrds, stockstats, pyfolio, pre-commit, lz4, jqdatasdk, gputil, exchange-calendars, elegantrl, ccxt, alpaca-trade-api, finrl\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: msgpack\n",
            "    Found existing installation: msgpack 1.0.3\n",
            "    Uninstalling msgpack-1.0.3:\n",
            "      Successfully uninstalled msgpack-1.0.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed aiodns-3.0.0 aiohttp-3.8.1 aiohttp-cors-0.7.0 aioredis-1.3.1 aiosignal-1.2.0 alpaca-trade-api-1.2.3 async-timeout-4.0.2 asynctest-0.13.0 blessed-1.19.0 box2d-py-2.3.8 ccxt-1.67.31 cfgv-3.3.1 colorful-0.5.4 cryptography-36.0.1 deprecated-1.2.13 distlib-0.3.4 elegantrl-0.3.3 empyrical-0.5.5 exchange-calendars-3.5 finrl-0.3.4 frozenlist-1.2.0 gpustat-1.0.0b1 gputil-1.4.0 hiredis-2.0.0 identify-2.4.3 jqdatasdk-1.8.10 lxml-4.7.1 lz4-3.1.10 mock-4.0.3 msgpack-1.0.2 multidict-5.2.0 nodeenv-1.6.0 opencensus-0.8.0 opencensus-context-0.1.2 platformdirs-2.4.1 ply-3.11 pre-commit-2.16.0 psycopg2-binary-2.9.3 py-spy-0.3.11 pybullet-3.2.1 pycares-4.1.2 pyfolio-0.9.2+75.g4b901f6 pyluach-1.3.0 pymysql-1.0.2 pyyaml-6.0 ray-1.9.2 redis-4.1.0 requests-2.27.1 stable-baselines3-1.3.0 stockstats-0.4.1 tensorboardX-2.4.1 thriftpy2-0.4.14 virtualenv-20.13.0 websocket-client-1.2.3 websockets-9.1 wrds-3.1.1 yarl-1.7.2 yfinance-0.1.69\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeMK7Uentj1V"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.apps import config\n",
        "from finrl.finrl_meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.finrl_meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.finrl_meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.drl_agents.stablebaselines3.models import DRLAgent,DRLEnsembleAgent\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "aa34532f-f67b-46ed-9089-6121dfa6c350"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "da7abd62-fc93-4151-e993-c653efae9caa"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "2e277f8a-9b01-4784-d088-e1b7e5dac184"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-07-06',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (91841, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GiRuFOTOtj1Y",
        "outputId": "5e683df2-eb13-4fd0-ce55-a5c6fa012ca8"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
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              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
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              "      <td>3.041429</td>\n",
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              "      <td>2009-01-02</td>\n",
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              "      <td>7010200</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.475792</td>\n",
              "      <td>7117200</td>\n",
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            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.778781  746015200  AAPL    4\n",
              "1  2009-01-02  58.590000  59.080002  57.750000  45.615868    6547900  AMGN    4\n",
              "2  2009-01-02  18.570000  19.520000  18.400000  15.579444   10955700   AXP    4\n",
              "3  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "4  2009-01-02  44.910000  46.980000  44.709999  32.475792    7117200   CAT    4"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
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      "metadata": {
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          "height": 206
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        "id": "DSw4ZEzVtj1Z",
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      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>91836</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>405.089996</td>\n",
              "      <td>409.869995</td>\n",
              "      <td>403.880005</td>\n",
              "      <td>406.601959</td>\n",
              "      <td>1982400</td>\n",
              "      <td>UNH</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91837</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>235.830002</td>\n",
              "      <td>238.779999</td>\n",
              "      <td>235.820007</td>\n",
              "      <td>237.886353</td>\n",
              "      <td>4383700</td>\n",
              "      <td>V</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91838</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>56.380001</td>\n",
              "      <td>56.570000</td>\n",
              "      <td>56.259998</td>\n",
              "      <td>54.501297</td>\n",
              "      <td>11387100</td>\n",
              "      <td>VZ</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91839</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>49.110001</td>\n",
              "      <td>49.139999</td>\n",
              "      <td>47.570000</td>\n",
              "      <td>47.243721</td>\n",
              "      <td>15863200</td>\n",
              "      <td>WBA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91840</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>139.350006</td>\n",
              "      <td>141.080002</td>\n",
              "      <td>139.350006</td>\n",
              "      <td>139.036484</td>\n",
              "      <td>8760200</td>\n",
              "      <td>WMT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-276bc56c-3609-4a54-aef1-5ce03f39ecb2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-276bc56c-3609-4a54-aef1-5ce03f39ecb2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-276bc56c-3609-4a54-aef1-5ce03f39ecb2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             date        open        high  ...    volume  tic  day\n",
              "91836  2021-07-02  405.089996  409.869995  ...   1982400  UNH    4\n",
              "91837  2021-07-02  235.830002  238.779999  ...   4383700    V    4\n",
              "91838  2021-07-02   56.380001   56.570000  ...  11387100   VZ    4\n",
              "91839  2021-07-02   49.110001   49.139999  ...  15863200  WBA    4\n",
              "91840  2021-07-02  139.350006  141.080002  ...   8760200  WMT    4\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "747763c8-1aff-4793-beb2-811e49fa6b66"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(91841, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4hYkeaPiICHS",
        "outputId": "9c32d488-4ee0-4235-8e77-b146dfabb691"
      },
      "source": [
        "df.sort_values(['date','tic']).head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
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              "      <th>day</th>\n",
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              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
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              "      <td>2009-01-02</td>\n",
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              "      <td>6547900</td>\n",
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              "      <td>4</td>\n",
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              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.579444</td>\n",
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              "      <td>4</td>\n",
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              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.475792</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e4e404ec-21c8-4050-a57f-86fee6510dd4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.778781  746015200  AAPL    4\n",
              "1  2009-01-02  58.590000  59.080002  57.750000  45.615868    6547900  AMGN    4\n",
              "2  2009-01-02  18.570000  19.520000  18.400000  15.579444   10955700   AXP    4\n",
              "3  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "4  2009-01-02  44.910000  46.980000  44.709999  32.475792    7117200   CAT    4"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2vryMsdNL9H",
        "outputId": "90fa5b4f-ae8b-4370-f635-c7ccec66ca66"
      },
      "source": [
        "len(df.tic.unique())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcNyXa7RNPrF",
        "outputId": "3f9d19d3-bd99-42d7-ecd7-a9d344e5e0ab"
      },
      "source": [
        "df.tic.value_counts()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MRK     3147\n",
              "AXP     3147\n",
              "CRM     3147\n",
              "CAT     3147\n",
              "NKE     3147\n",
              "JPM     3147\n",
              "V       3147\n",
              "JNJ     3147\n",
              "MCD     3147\n",
              "AAPL    3147\n",
              "BA      3147\n",
              "KO      3147\n",
              "VZ      3147\n",
              "HD      3147\n",
              "DIS     3147\n",
              "CVX     3147\n",
              "GS      3147\n",
              "WMT     3147\n",
              "TRV     3147\n",
              "AMGN    3147\n",
              "INTC    3147\n",
              "PG      3147\n",
              "MMM     3147\n",
              "WBA     3147\n",
              "CSCO    3147\n",
              "IBM     3147\n",
              "HON     3147\n",
              "MSFT    3147\n",
              "UNH     3147\n",
              "DOW      578\n",
              "Name: tic, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM5bH9uroCeg"
      },
      "source": [
        "tech_indicators = ['macd',\n",
        " 'rsi_30',\n",
        " 'cci_30',\n",
        " 'dx_30']"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgXfBcjxtj1a",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "6a640d55-4c93-45e6-be82-95538f68ce19"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = tech_indicators,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)\n",
        "processed = processed.copy()\n",
        "processed = processed.fillna(0)\n",
        "processed = processed.replace(np.inf,0)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "grvhGJJII3Xn",
        "outputId": "b6c15ee2-30df-4c19-fc5a-a61377ba995b"
      },
      "source": [
        "processed.sample(5)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>79483</th>\n",
              "      <td>2019-11-20</td>\n",
              "      <td>134.779999</td>\n",
              "      <td>135.699997</td>\n",
              "      <td>134.440002</td>\n",
              "      <td>128.119186</td>\n",
              "      <td>1301100</td>\n",
              "      <td>TRV</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.941615</td>\n",
              "      <td>45.143400</td>\n",
              "      <td>5.895652</td>\n",
              "      <td>8.540383</td>\n",
              "      <td>17.348481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>723</th>\n",
              "      <td>2009-02-06</td>\n",
              "      <td>27.639999</td>\n",
              "      <td>28.389999</td>\n",
              "      <td>27.410000</td>\n",
              "      <td>20.467070</td>\n",
              "      <td>8531100</td>\n",
              "      <td>WBA</td>\n",
              "      <td>4</td>\n",
              "      <td>0.175216</td>\n",
              "      <td>62.370571</td>\n",
              "      <td>156.344891</td>\n",
              "      <td>31.850314</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73002</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>170.660004</td>\n",
              "      <td>171.770004</td>\n",
              "      <td>168.289993</td>\n",
              "      <td>159.382019</td>\n",
              "      <td>4060200</td>\n",
              "      <td>GS</td>\n",
              "      <td>3</td>\n",
              "      <td>-7.324565</td>\n",
              "      <td>37.905677</td>\n",
              "      <td>-54.343755</td>\n",
              "      <td>15.405786</td>\n",
              "      <td>52.771747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19220</th>\n",
              "      <td>2011-08-18</td>\n",
              "      <td>60.820000</td>\n",
              "      <td>61.270000</td>\n",
              "      <td>60.049999</td>\n",
              "      <td>44.637592</td>\n",
              "      <td>17846200</td>\n",
              "      <td>PG</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.431899</td>\n",
              "      <td>44.430812</td>\n",
              "      <td>-66.168684</td>\n",
              "      <td>32.078905</td>\n",
              "      <td>73.941353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51415</th>\n",
              "      <td>2016-01-19</td>\n",
              "      <td>80.919998</td>\n",
              "      <td>81.639999</td>\n",
              "      <td>79.860001</td>\n",
              "      <td>67.845657</td>\n",
              "      <td>6292100</td>\n",
              "      <td>WBA</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.801511</td>\n",
              "      <td>45.738959</td>\n",
              "      <td>-95.128637</td>\n",
              "      <td>20.046673</td>\n",
              "      <td>40.271704</td>\n",
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            "text/plain": [
              "             date        open        high  ...      cci_30      dx_30  turbulence\n",
              "79483  2019-11-20  134.779999  135.699997  ...    5.895652   8.540383   17.348481\n",
              "723    2009-02-06   27.639999   28.389999  ...  156.344891  31.850314    0.000000\n",
              "73002  2019-01-03  170.660004  171.770004  ...  -54.343755  15.405786   52.771747\n",
              "19220  2011-08-18   60.820000   61.270000  ...  -66.168684  32.078905   73.941353\n",
              "51415  2016-01-19   80.919998   81.639999  ...  -95.128637  20.046673   40.271704\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "dcca6027-3f21-42bb-dac6-7f7f135d4137"
      },
      "source": [
        "stock_dimension = len(processed.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(tech_indicators)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock Dimension: 29, State Space: 175\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"buy_cost_pct\": 0.001, \n",
        "    \"sell_cost_pct\": 0.001, \n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": tech_indicators,\n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4,\n",
        "    \"print_verbosity\":5\n",
        "    \n",
        "}"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms.\n",
        "\n",
        "* In this notebook, we are training and validating 3 agents (A2C, PPO, DDPG) using Rolling-window Ensemble Method ([reference code](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020/blob/80415db8fa7b2179df6bd7e81ce4fe8dbf913806/model/models.py#L92))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-gthCxMtj1d"
      },
      "source": [
        "rebalance_window = 63 # rebalance_window is the number of days to retrain the model\n",
        "validation_window = 63 # validation_window is the number of days to do validation and trading (e.g. if validation_window=63, then both validation and trading period will be 63 days)\n",
        "train_start = '2009-01-01'\n",
        "train_end = '2020-04-01'\n",
        "val_test_start = '2020-04-01'\n",
        "val_test_end = '2021-07-20'\n",
        "\n",
        "ensemble_agent = DRLEnsembleAgent(df=processed,\n",
        "                 train_period=(train_start,train_end),\n",
        "                 val_test_period=(val_test_start,val_test_end),\n",
        "                 rebalance_window=rebalance_window, \n",
        "                 validation_window=validation_window, \n",
        "                 **env_kwargs)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsfEHa_Etj1d",
        "scrolled": false
      },
      "source": [
        "A2C_model_kwargs = {\n",
        "                    'n_steps': 5,\n",
        "                    'ent_coef': 0.01,\n",
        "                    'learning_rate': 0.0005\n",
        "                    }\n",
        "\n",
        "PPO_model_kwargs = {\n",
        "                    \"ent_coef\":0.01,\n",
        "                    \"n_steps\": 2048,\n",
        "                    \"learning_rate\": 0.00025,\n",
        "                    \"batch_size\": 64\n",
        "                    }\n",
        "\n",
        "DDPG_model_kwargs = {\n",
        "                      #\"action_noise\":\"ornstein_uhlenbeck\",\n",
        "                      \"buffer_size\": 100_000,\n",
        "                      \"learning_rate\": 0.000005,\n",
        "                      \"batch_size\": 64\n",
        "                    }\n",
        "\n",
        "timesteps_dict = {'a2c' : 30_000, \n",
        "                 'ppo' : 100_000, \n",
        "                 'ddpg' : 10_000\n",
        "                 }\n",
        "\n",
        "\n",
        "timesteps_dict = {'a2c' : 10_000, \n",
        "                 'ppo' : 10_000, \n",
        "                 'ddpg' : 10_000\n",
        "                 }"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1lyCECstj1e",
        "scrolled": true,
        "outputId": "e5f90c65-7b7c-448f-907f-2ccdfba951ac"
      },
      "source": [
        "df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,\n",
        "                                                 PPO_model_kwargs,\n",
        "                                                 DDPG_model_kwargs,\n",
        "                                                 timesteps_dict)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============Start Ensemble Strategy============\n",
            "============================================\n",
            "turbulence_threshold:  200.92993079372604\n",
            "======Model training from:  2009-01-01 to  2020-04-02\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_126_1\n",
            "----------------------------------------\n",
            "| time/                 |              |\n",
            "|    fps                | 59           |\n",
            "|    iterations         | 100          |\n",
            "|    time_elapsed       | 8            |\n",
            "|    total_timesteps    | 500          |\n",
            "| train/                |              |\n",
            "|    entropy_loss       | -41.3        |\n",
            "|    explained_variance | -0.0328      |\n",
            "|    learning_rate      | 0.0005       |\n",
            "|    n_updates          | 99           |\n",
            "|    policy_loss        | -7.49        |\n",
            "|    reward             | -0.051769912 |\n",
            "|    std                | 1            |\n",
            "|    value_loss         | 0.344        |\n",
            "----------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 70         |\n",
            "|    iterations         | 200        |\n",
            "|    time_elapsed       | 14         |\n",
            "|    total_timesteps    | 1000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0.0206     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 199        |\n",
            "|    policy_loss        | -122       |\n",
            "|    reward             | -1.4591484 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 9.47       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 75        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 19        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0.000771  |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -404      |\n",
            "|    reward             | 6.2669177 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 86.4      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 78        |\n",
            "|    iterations         | 400       |\n",
            "|    time_elapsed       | 25        |\n",
            "|    total_timesteps    | 2000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 399       |\n",
            "|    policy_loss        | -41.2     |\n",
            "|    reward             | 2.9870415 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 8.41      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 80        |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 31        |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0.00274   |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 470       |\n",
            "|    reward             | -8.643984 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 133       |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 81         |\n",
            "|    iterations         | 600        |\n",
            "|    time_elapsed       | 36         |\n",
            "|    total_timesteps    | 3000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -0.808     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 599        |\n",
            "|    policy_loss        | 34.4       |\n",
            "|    reward             | 0.82639563 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 2.14       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 83        |\n",
            "|    iterations         | 700       |\n",
            "|    time_elapsed       | 42        |\n",
            "|    total_timesteps    | 3500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0.0166    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 699       |\n",
            "|    policy_loss        | 39.5      |\n",
            "|    reward             | 1.6315455 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.53      |\n",
            "-------------------------------------\n",
            "----------------------------------------\n",
            "| time/                 |              |\n",
            "|    fps                | 83           |\n",
            "|    iterations         | 800          |\n",
            "|    time_elapsed       | 47           |\n",
            "|    total_timesteps    | 4000         |\n",
            "| train/                |              |\n",
            "|    entropy_loss       | -41.2        |\n",
            "|    explained_variance | -1.19e-07    |\n",
            "|    learning_rate      | 0.0005       |\n",
            "|    n_updates          | 799          |\n",
            "|    policy_loss        | -107         |\n",
            "|    reward             | -0.075020224 |\n",
            "|    std                | 1            |\n",
            "|    value_loss         | 10.4         |\n",
            "----------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 84         |\n",
            "|    iterations         | 900        |\n",
            "|    time_elapsed       | 53         |\n",
            "|    total_timesteps    | 4500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -0.16      |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 899        |\n",
            "|    policy_loss        | 12.6       |\n",
            "|    reward             | -2.6403275 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 0.708      |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 1000      |\n",
            "|    time_elapsed       | 58        |\n",
            "|    total_timesteps    | 5000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -0.0825   |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 999       |\n",
            "|    policy_loss        | 9.93      |\n",
            "|    reward             | 0.6558525 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.33      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 64        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.132    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | -567      |\n",
            "|    reward             | -7.434152 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 206       |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1200       |\n",
            "|    time_elapsed       | 69         |\n",
            "|    total_timesteps    | 6000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0.0284     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1199       |\n",
            "|    policy_loss        | -52.7      |\n",
            "|    reward             | -2.3101468 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 2.58       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1300       |\n",
            "|    time_elapsed       | 75         |\n",
            "|    total_timesteps    | 6500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1299       |\n",
            "|    policy_loss        | -115       |\n",
            "|    reward             | 0.21878022 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 9.42       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1400       |\n",
            "|    time_elapsed       | 80         |\n",
            "|    total_timesteps    | 7000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1399       |\n",
            "|    policy_loss        | 53.8       |\n",
            "|    reward             | 0.87620676 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 3.67       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 86        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -0.126    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | 128       |\n",
            "|    reward             | -1.743883 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 13        |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 91       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | -0.181   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 31.5     |\n",
            "|    reward             | 2.94982  |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 21       |\n",
            "------------------------------------\n",
            "------------------------------------------\n",
            "| time/                 |                |\n",
            "|    fps                | 87             |\n",
            "|    iterations         | 1700           |\n",
            "|    time_elapsed       | 97             |\n",
            "|    total_timesteps    | 8500           |\n",
            "| train/                |                |\n",
            "|    entropy_loss       | -41.4          |\n",
            "|    explained_variance | -0.0969        |\n",
            "|    learning_rate      | 0.0005         |\n",
            "|    n_updates          | 1699           |\n",
            "|    policy_loss        | -1.06e+03      |\n",
            "|    reward             | -0.00018015769 |\n",
            "|    std                | 1.01           |\n",
            "|    value_loss         | 1.26e+03       |\n",
            "------------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 102       |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0.32      |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | -3.74     |\n",
            "|    reward             | -0.731615 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 0.0184    |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 108       |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | -94       |\n",
            "|    reward             | -0.577778 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 6.25      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 88         |\n",
            "|    iterations         | 2000       |\n",
            "|    time_elapsed       | 113        |\n",
            "|    total_timesteps    | 10000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 0.0354     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1999       |\n",
            "|    policy_loss        | 268        |\n",
            "|    reward             | -0.7198939 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 52.7       |\n",
            "--------------------------------------\n",
            "======A2C Validation from:  2020-04-02 to  2020-07-02\n",
            "A2C Sharpe Ratio:  0.25033460143894093\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_126_1\n",
            "------------------------------------\n",
            "| time/              |             |\n",
            "|    fps             | 97          |\n",
            "|    iterations      | 1           |\n",
            "|    time_elapsed    | 21          |\n",
            "|    total_timesteps | 2048        |\n",
            "| train/             |             |\n",
            "|    reward          | -0.04487341 |\n",
            "------------------------------------\n",
            "day: 2830, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2196151.30\n",
            "total_reward: 1196151.30\n",
            "total_cost: 335848.27\n",
            "total_trades: 78804\n",
            "Sharpe: 0.497\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 94          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 43          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018230084 |\n",
            "|    clip_fraction        | 0.245       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00354    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.6         |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0305     |\n",
            "|    reward               | -1.5173061  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 14.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 93          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 65          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019515125 |\n",
            "|    clip_fraction        | 0.233       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | -0.0247     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 55.8        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0225     |\n",
            "|    reward               | 2.814237    |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 54.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 92          |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 88          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024547426 |\n",
            "|    clip_fraction        | 0.261       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | 0.00199     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 22.5        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0196     |\n",
            "|    reward               | 1.2317431   |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 49.9        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 92          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 110         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025651447 |\n",
            "|    clip_fraction        | 0.245       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.4       |\n",
            "|    explained_variance   | -0.0224     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 11.2        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0189     |\n",
            "|    reward               | 1.1878457   |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 28.9        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-04-02 to  2020-07-02\n",
            "PPO Sharpe Ratio:  0.19630374643213191\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_126_1\n",
            "day: 2830, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2902215.69\n",
            "total_reward: 1902215.69\n",
            "total_cost: 1846.50\n",
            "total_trades: 39965\n",
            "Sharpe: 0.629\n",
            "=================================\n",
            "-----------------------------------\n",
            "| time/              |            |\n",
            "|    episodes        | 4          |\n",
            "|    fps             | 44         |\n",
            "|    time_elapsed    | 255        |\n",
            "|    total_timesteps | 11324      |\n",
            "| train/             |            |\n",
            "|    actor_loss      | 45.4       |\n",
            "|    critic_loss     | 391        |\n",
            "|    learning_rate   | 5e-06      |\n",
            "|    n_updates       | 8493       |\n",
            "|    reward          | -13.366796 |\n",
            "-----------------------------------\n",
            "======DDPG Validation from:  2020-04-02 to  2020-07-02\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-07-02\n",
            "======Trading from:  2020-07-02 to  2020-10-01\n",
            "============================================\n",
            "turbulence_threshold:  200.92993079372604\n",
            "======Model training from:  2009-01-01 to  2020-07-02\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_189_1\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 100        |\n",
            "|    time_elapsed       | 5          |\n",
            "|    total_timesteps    | 500        |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -0.0404    |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 99         |\n",
            "|    policy_loss        | -42.7      |\n",
            "|    reward             | 0.27108225 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 1.71       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 200        |\n",
            "|    time_elapsed       | 11         |\n",
            "|    total_timesteps    | 1000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.036      |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 199        |\n",
            "|    policy_loss        | 4.02       |\n",
            "|    reward             | -1.6978885 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 1.61       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 17        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -0.797    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -336      |\n",
            "|    reward             | 7.4013658 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 72.2      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 400        |\n",
            "|    time_elapsed       | 23         |\n",
            "|    total_timesteps    | 2000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -0.0249    |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 399        |\n",
            "|    policy_loss        | 144        |\n",
            "|    reward             | 0.18983567 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 14.7       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 29        |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0.00651   |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 399       |\n",
            "|    reward             | -5.953611 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 113       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 600       |\n",
            "|    time_elapsed       | 34        |\n",
            "|    total_timesteps    | 3000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -1.17     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 599       |\n",
            "|    policy_loss        | 81.1      |\n",
            "|    reward             | 0.2980301 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 6.01      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 700       |\n",
            "|    time_elapsed       | 40        |\n",
            "|    total_timesteps    | 3500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0.546     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 699       |\n",
            "|    policy_loss        | -59.3     |\n",
            "|    reward             | 1.7133454 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 2.1       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 800       |\n",
            "|    time_elapsed       | 46        |\n",
            "|    total_timesteps    | 4000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0.0664    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 799       |\n",
            "|    policy_loss        | 36.4      |\n",
            "|    reward             | 1.9195006 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 2.08      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 900        |\n",
            "|    time_elapsed       | 52         |\n",
            "|    total_timesteps    | 4500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -0.037     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 899        |\n",
            "|    policy_loss        | 124        |\n",
            "|    reward             | 0.44107372 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 10.7       |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 86          |\n",
            "|    iterations         | 1000        |\n",
            "|    time_elapsed       | 57          |\n",
            "|    total_timesteps    | 5000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0005      |\n",
            "|    n_updates          | 999         |\n",
            "|    policy_loss        | -46.1       |\n",
            "|    reward             | -0.85359025 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 3.56        |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1100       |\n",
            "|    time_elapsed       | 63         |\n",
            "|    total_timesteps    | 5500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0.0407     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1099       |\n",
            "|    policy_loss        | -96.3      |\n",
            "|    reward             | -11.516248 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 14         |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 86          |\n",
            "|    iterations         | 1200        |\n",
            "|    time_elapsed       | 69          |\n",
            "|    total_timesteps    | 6000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | -0.0572     |\n",
            "|    learning_rate      | 0.0005      |\n",
            "|    n_updates          | 1199        |\n",
            "|    policy_loss        | -82.4       |\n",
            "|    reward             | 0.005680397 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 6           |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1300       |\n",
            "|    time_elapsed       | 75         |\n",
            "|    total_timesteps    | 6500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -0.378     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1299       |\n",
            "|    policy_loss        | 100        |\n",
            "|    reward             | 0.16837326 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 9.2        |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1400      |\n",
            "|    time_elapsed       | 81        |\n",
            "|    total_timesteps    | 7000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0.108     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1399      |\n",
            "|    policy_loss        | 56.7      |\n",
            "|    reward             | 0.2755015 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 2.95      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1500       |\n",
            "|    time_elapsed       | 87         |\n",
            "|    total_timesteps    | 7500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -0.00349   |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1499       |\n",
            "|    policy_loss        | -270       |\n",
            "|    reward             | -0.9525704 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 42.1       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1600       |\n",
            "|    time_elapsed       | 92         |\n",
            "|    total_timesteps    | 8000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0.39       |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1599       |\n",
            "|    policy_loss        | 56.1       |\n",
            "|    reward             | -0.5067834 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 2.25       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 98        |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 5.96e-08  |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | 42.3      |\n",
            "|    reward             | 2.8328996 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 26.3      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 104       |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0.148     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | 2.71      |\n",
            "|    reward             | 1.2313448 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 0.228     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 110       |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.842    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | 108       |\n",
            "|    reward             | 1.0191236 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 7.19      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 115      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | -0.166   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | -79.9    |\n",
            "|    reward             | 3.596779 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.47     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2020-07-02 to  2020-10-01\n",
            "A2C Sharpe Ratio:  0.3324629886764393\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_189_1\n",
            "-------------------------------------\n",
            "| time/              |              |\n",
            "|    fps             | 93           |\n",
            "|    iterations      | 1            |\n",
            "|    time_elapsed    | 22           |\n",
            "|    total_timesteps | 2048         |\n",
            "| train/             |              |\n",
            "|    reward          | -0.085719466 |\n",
            "-------------------------------------\n",
            "day: 2893, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3087465.25\n",
            "total_reward: 2087465.25\n",
            "total_cost: 354063.72\n",
            "total_trades: 80864\n",
            "Sharpe: 0.626\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 45          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019241996 |\n",
            "|    clip_fraction        | 0.246       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.021      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.07        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0243     |\n",
            "|    reward               | 1.500187    |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 14.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 68          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014936658 |\n",
            "|    clip_fraction        | 0.147       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | 0.0165      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 63.9        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0182     |\n",
            "|    reward               | 0.18779926  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 95.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 91          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019090343 |\n",
            "|    clip_fraction        | 0.208       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | 0.00491     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 19.6        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0183     |\n",
            "|    reward               | -1.1587315  |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 85.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 114         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.021126334 |\n",
            "|    clip_fraction        | 0.254       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | 0.0162      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 11.6        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0232     |\n",
            "|    reward               | -2.061006   |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 20.6        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-07-02 to  2020-10-01\n",
            "PPO Sharpe Ratio:  0.12120565668544925\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_189_1\n",
            "day: 2893, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2726077.37\n",
            "total_reward: 1726077.37\n",
            "total_cost: 1765.83\n",
            "total_trades: 41996\n",
            "Sharpe: 0.558\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 43        |\n",
            "|    time_elapsed    | 264       |\n",
            "|    total_timesteps | 11576     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | 88.8      |\n",
            "|    critic_loss     | 640       |\n",
            "|    learning_rate   | 5e-06     |\n",
            "|    n_updates       | 8682      |\n",
            "|    reward          | 2.7025018 |\n",
            "----------------------------------\n",
            "======DDPG Validation from:  2020-07-02 to  2020-10-01\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-10-01\n",
            "======Trading from:  2020-10-01 to  2020-12-31\n",
            "============================================\n",
            "turbulence_threshold:  200.92993079372604\n",
            "======Model training from:  2009-01-01 to  2020-10-01\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_252_1\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 79          |\n",
            "|    iterations         | 100         |\n",
            "|    time_elapsed       | 6           |\n",
            "|    total_timesteps    | 500         |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | -0.125      |\n",
            "|    learning_rate      | 0.0005      |\n",
            "|    n_updates          | 99          |\n",
            "|    policy_loss        | -35.5       |\n",
            "|    reward             | -0.09041673 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 2.62        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 75        |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 13        |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.077    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | -14.5     |\n",
            "|    reward             | 0.3587879 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 0.584     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 78        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 19        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.0709   |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -253      |\n",
            "|    reward             | 4.5632515 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 46.9      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 80        |\n",
            "|    iterations         | 400       |\n",
            "|    time_elapsed       | 24        |\n",
            "|    total_timesteps    | 2000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 399       |\n",
            "|    policy_loss        | -21.1     |\n",
            "|    reward             | 1.0681273 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 4.81      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 81         |\n",
            "|    iterations         | 500        |\n",
            "|    time_elapsed       | 30         |\n",
            "|    total_timesteps    | 2500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -0.139     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 499        |\n",
            "|    policy_loss        | 423        |\n",
            "|    reward             | -6.0229154 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 131        |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 82         |\n",
            "|    iterations         | 600        |\n",
            "|    time_elapsed       | 36         |\n",
            "|    total_timesteps    | 3000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | -0.000959  |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 599        |\n",
            "|    policy_loss        | -6.28      |\n",
            "|    reward             | -1.3853685 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 1.27       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 82        |\n",
            "|    iterations         | 700       |\n",
            "|    time_elapsed       | 42        |\n",
            "|    total_timesteps    | 3500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -0.522    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 699       |\n",
            "|    policy_loss        | -56       |\n",
            "|    reward             | -1.983197 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 2.56      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 82        |\n",
            "|    iterations         | 800       |\n",
            "|    time_elapsed       | 48        |\n",
            "|    total_timesteps    | 4000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -1.46     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 799       |\n",
            "|    policy_loss        | -36.1     |\n",
            "|    reward             | 1.8168931 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 1.48      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 82        |\n",
            "|    iterations         | 900       |\n",
            "|    time_elapsed       | 54        |\n",
            "|    total_timesteps    | 4500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 899       |\n",
            "|    policy_loss        | -30.7     |\n",
            "|    reward             | 2.7901127 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 4.68      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 83        |\n",
            "|    iterations         | 1000      |\n",
            "|    time_elapsed       | 60        |\n",
            "|    total_timesteps    | 5000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -0.0115   |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 999       |\n",
            "|    policy_loss        | 96.8      |\n",
            "|    reward             | 2.1106558 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 16.5      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 83        |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 65        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0.188     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | 605       |\n",
            "|    reward             | 0.8484186 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 210       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 83        |\n",
            "|    iterations         | 1200      |\n",
            "|    time_elapsed       | 71        |\n",
            "|    total_timesteps    | 6000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -0.202    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1199      |\n",
            "|    policy_loss        | 81.4      |\n",
            "|    reward             | -2.480206 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 5.93      |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 83          |\n",
            "|    iterations         | 1300        |\n",
            "|    time_elapsed       | 77          |\n",
            "|    total_timesteps    | 6500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.4       |\n",
            "|    explained_variance | -1.19e-07   |\n",
            "|    learning_rate      | 0.0005      |\n",
            "|    n_updates          | 1299        |\n",
            "|    policy_loss        | 43.9        |\n",
            "|    reward             | -0.26437733 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 3.52        |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 83          |\n",
            "|    iterations         | 1400        |\n",
            "|    time_elapsed       | 83          |\n",
            "|    total_timesteps    | 7000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.4       |\n",
            "|    explained_variance | 0.00658     |\n",
            "|    learning_rate      | 0.0005      |\n",
            "|    n_updates          | 1399        |\n",
            "|    policy_loss        | 181         |\n",
            "|    reward             | -0.63620466 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 26          |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 83         |\n",
            "|    iterations         | 1500       |\n",
            "|    time_elapsed       | 89         |\n",
            "|    total_timesteps    | 7500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | -0.0235    |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1499       |\n",
            "|    policy_loss        | 270        |\n",
            "|    reward             | -0.5455963 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 40.8       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 83        |\n",
            "|    iterations         | 1600      |\n",
            "|    time_elapsed       | 95        |\n",
            "|    total_timesteps    | 8000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -0.112    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1599      |\n",
            "|    policy_loss        | -52.3     |\n",
            "|    reward             | -2.071636 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 4.2       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 83        |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 101       |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.0406   |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | 322       |\n",
            "|    reward             | 0.5182562 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 70.6      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 83         |\n",
            "|    iterations         | 1800       |\n",
            "|    time_elapsed       | 107        |\n",
            "|    total_timesteps    | 9000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | -0.0319    |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1799       |\n",
            "|    policy_loss        | -0.937     |\n",
            "|    reward             | 0.67256796 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 0.705      |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 83        |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 113       |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | -153      |\n",
            "|    reward             | 1.9791864 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 14.5      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 83         |\n",
            "|    iterations         | 2000       |\n",
            "|    time_elapsed       | 119        |\n",
            "|    total_timesteps    | 10000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 0.0338     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1999       |\n",
            "|    policy_loss        | -123       |\n",
            "|    reward             | -1.0705515 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 17.1       |\n",
            "--------------------------------------\n",
            "======A2C Validation from:  2020-10-01 to  2020-12-31\n",
            "A2C Sharpe Ratio:  0.157398297864907\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_252_1\n",
            "-----------------------------------\n",
            "| time/              |            |\n",
            "|    fps             | 91         |\n",
            "|    iterations      | 1          |\n",
            "|    time_elapsed    | 22         |\n",
            "|    total_timesteps | 2048       |\n",
            "| train/             |            |\n",
            "|    reward          | 0.30793157 |\n",
            "-----------------------------------\n",
            "day: 2956, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3942705.65\n",
            "total_reward: 2942705.65\n",
            "total_cost: 374440.05\n",
            "total_trades: 82889\n",
            "Sharpe: 0.797\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 46          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017636795 |\n",
            "|    clip_fraction        | 0.207       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00652    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.89        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0222     |\n",
            "|    reward               | 0.14401561  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 11.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 88          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 69          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019762876 |\n",
            "|    clip_fraction        | 0.223       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00246    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 17.1        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0192     |\n",
            "|    reward               | 1.1803926   |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 50.7        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 88         |\n",
            "|    iterations           | 4          |\n",
            "|    time_elapsed         | 92         |\n",
            "|    total_timesteps      | 8192       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01684109 |\n",
            "|    clip_fraction        | 0.199      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -41.2      |\n",
            "|    explained_variance   | -0.0116    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 77.2       |\n",
            "|    n_updates            | 30         |\n",
            "|    policy_gradient_loss | -0.0217    |\n",
            "|    reward               | 1.0463831  |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 68.2       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 88          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 115         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024190305 |\n",
            "|    clip_fraction        | 0.278       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | -0.0346     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.4         |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0212     |\n",
            "|    reward               | -1.2749629  |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 19.7        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-10-01 to  2020-12-31\n",
            "PPO Sharpe Ratio:  0.20250342417658126\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_252_1\n",
            "day: 2956, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3957618.19\n",
            "total_reward: 2957618.19\n",
            "total_cost: 1601.16\n",
            "total_trades: 56009\n",
            "Sharpe: 0.762\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 44       |\n",
            "|    time_elapsed    | 268      |\n",
            "|    total_timesteps | 11828    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -5.37    |\n",
            "|    critic_loss     | 178      |\n",
            "|    learning_rate   | 5e-06    |\n",
            "|    n_updates       | 8871     |\n",
            "|    reward          | 4.856528 |\n",
            "---------------------------------\n",
            "======DDPG Validation from:  2020-10-01 to  2020-12-31\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-12-31\n",
            "======Trading from:  2020-12-31 to  2021-04-05\n",
            "============================================\n",
            "turbulence_threshold:  200.92993079372604\n",
            "======Model training from:  2009-01-01 to  2020-12-31\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_315_1\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 84        |\n",
            "|    iterations         | 100       |\n",
            "|    time_elapsed       | 5         |\n",
            "|    total_timesteps    | 500       |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -0.828    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 99        |\n",
            "|    policy_loss        | -18       |\n",
            "|    reward             | 0.2629864 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 0.645     |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 200        |\n",
            "|    time_elapsed       | 11         |\n",
            "|    total_timesteps    | 1000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -0.00115   |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 199        |\n",
            "|    policy_loss        | -57        |\n",
            "|    reward             | -1.3294735 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 2.91       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | -0.0673  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -330     |\n",
            "|    reward             | 5.483449 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 70.3     |\n",
            "------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 400         |\n",
            "|    time_elapsed       | 23          |\n",
            "|    total_timesteps    | 2000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | -0.0331     |\n",
            "|    learning_rate      | 0.0005      |\n",
            "|    n_updates          | 399         |\n",
            "|    policy_loss        | -86.5       |\n",
            "|    reward             | -0.29608092 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 19.8        |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 500        |\n",
            "|    time_elapsed       | 29         |\n",
            "|    total_timesteps    | 2500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 1.76e-05   |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 499        |\n",
            "|    policy_loss        | 746        |\n",
            "|    reward             | -11.729683 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 387        |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 600       |\n",
            "|    time_elapsed       | 35        |\n",
            "|    total_timesteps    | 3000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0.338     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 599       |\n",
            "|    policy_loss        | 170       |\n",
            "|    reward             | -16.74081 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 26        |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 700        |\n",
            "|    time_elapsed       | 41         |\n",
            "|    total_timesteps    | 3500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 699        |\n",
            "|    policy_loss        | 71.1       |\n",
            "|    reward             | -0.4083204 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 4.58       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 46       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | 0.0553   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -99.9    |\n",
            "|    reward             | 2.392244 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 52       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | -0.378   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 83.9     |\n",
            "|    reward             | 2.506633 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 7.72     |\n",
            "------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 1000       |\n",
            "|    time_elapsed       | 58         |\n",
            "|    total_timesteps    | 5000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | -0.595     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 999        |\n",
            "|    policy_loss        | -67.9      |\n",
            "|    reward             | 0.69298357 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 4.27       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 64        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -0.563    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | 120       |\n",
            "|    reward             | 10.568737 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 13.2      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 70       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | -0.0285  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -607     |\n",
            "|    reward             | 6.724224 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 200      |\n",
            "------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 1300        |\n",
            "|    time_elapsed       | 75          |\n",
            "|    total_timesteps    | 6500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | 0.0284      |\n",
            "|    learning_rate      | 0.0005      |\n",
            "|    n_updates          | 1299        |\n",
            "|    policy_loss        | 22.8        |\n",
            "|    reward             | -0.28616953 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 0.979       |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 1400       |\n",
            "|    time_elapsed       | 81         |\n",
            "|    total_timesteps    | 7000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.0225     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1399       |\n",
            "|    policy_loss        | 35.3       |\n",
            "|    reward             | -4.7152042 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 1.87       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 87       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | -0.0126  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | -225     |\n",
            "|    reward             | 4.668683 |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 47.6     |\n",
            "------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 1600       |\n",
            "|    time_elapsed       | 93         |\n",
            "|    total_timesteps    | 8000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0.0403     |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1599       |\n",
            "|    policy_loss        | -93.7      |\n",
            "|    reward             | 0.66578263 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 5.76       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 1700       |\n",
            "|    time_elapsed       | 99         |\n",
            "|    total_timesteps    | 8500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -0.16      |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1699       |\n",
            "|    policy_loss        | 220        |\n",
            "|    reward             | -11.292281 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 35.6       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 1800       |\n",
            "|    time_elapsed       | 105        |\n",
            "|    total_timesteps    | 9000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1799       |\n",
            "|    policy_loss        | 54.6       |\n",
            "|    reward             | -2.0646114 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 15.3       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 110       |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.483    |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | 36.2      |\n",
            "|    reward             | 1.1640425 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 2.17      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 2000       |\n",
            "|    time_elapsed       | 116        |\n",
            "|    total_timesteps    | 10000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0005     |\n",
            "|    n_updates          | 1999       |\n",
            "|    policy_loss        | 114        |\n",
            "|    reward             | -1.5702732 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 11.2       |\n",
            "--------------------------------------\n",
            "======A2C Validation from:  2020-12-31 to  2021-04-05\n",
            "A2C Sharpe Ratio:  0.1821600849726842\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_315_1\n",
            "-----------------------------------\n",
            "| time/              |            |\n",
            "|    fps             | 91         |\n",
            "|    iterations      | 1          |\n",
            "|    time_elapsed    | 22         |\n",
            "|    total_timesteps | 2048       |\n",
            "| train/             |            |\n",
            "|    reward          | 0.12615822 |\n",
            "-----------------------------------\n",
            "day: 3019, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4990034.90\n",
            "total_reward: 3990034.90\n",
            "total_cost: 391126.43\n",
            "total_trades: 84743\n",
            "Sharpe: 0.902\n",
            "=================================\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 89         |\n",
            "|    iterations           | 2          |\n",
            "|    time_elapsed         | 45         |\n",
            "|    total_timesteps      | 4096       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01619377 |\n",
            "|    clip_fraction        | 0.236      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -41.2      |\n",
            "|    explained_variance   | 0.0004     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 6.52       |\n",
            "|    n_updates            | 10         |\n",
            "|    policy_gradient_loss | -0.0221    |\n",
            "|    reward               | -3.2302363 |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 15.6       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 88          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 69          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016321607 |\n",
            "|    clip_fraction        | 0.186       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | 0.00344     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 53.1        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0126     |\n",
            "|    reward               | -0.37026227 |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 68.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 88          |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 92          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018890683 |\n",
            "|    clip_fraction        | 0.221       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | 0.00564     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 20.5        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0192     |\n",
            "|    reward               | -0.12967756 |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 66.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 88          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 116         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025792193 |\n",
            "|    clip_fraction        | 0.284       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.4       |\n",
            "|    explained_variance   | -0.0129     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.81        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0253     |\n",
            "|    reward               | 0.29261595  |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 10.1        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-12-31 to  2021-04-05\n",
            "PPO Sharpe Ratio:  0.29777940379649687\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_315_1\n",
            "day: 3019, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 6083808.47\n",
            "total_reward: 5083808.47\n",
            "total_cost: 1441.78\n",
            "total_trades: 41663\n",
            "Sharpe: 0.886\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 44       |\n",
            "|    time_elapsed    | 274      |\n",
            "|    total_timesteps | 12080    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 36.3     |\n",
            "|    critic_loss     | 46.9     |\n",
            "|    learning_rate   | 5e-06    |\n",
            "|    n_updates       | 9060     |\n",
            "|    reward          | 1.169599 |\n",
            "---------------------------------\n",
            "======DDPG Validation from:  2020-12-31 to  2021-04-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2021-04-05\n",
            "======Trading from:  2021-04-05 to  2021-07-02\n",
            "Ensemble Strategy took:  36.559605312347415  minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
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        "id": "-0qd8acMtj1f",
        "outputId": "ceaee494-5015-42cf-ee19-45665b4a4ac2"
      },
      "source": [
        "df_summary"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Iter</th>\n",
              "      <th>Val Start</th>\n",
              "      <th>Val End</th>\n",
              "      <th>Model Used</th>\n",
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              "      <td>2020-12-31</td>\n",
              "      <td>2021-04-05</td>\n",
              "      <td>PPO</td>\n",
              "      <td>0.18216</td>\n",
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              "  "
            ],
            "text/plain": [
              "  Iter   Val Start     Val End Model Used A2C Sharpe PPO Sharpe DDPG Sharpe\n",
              "0  126  2020-04-02  2020-07-02       DDPG   0.250335   0.196304    0.279741\n",
              "1  189  2020-07-02  2020-10-01        A2C   0.332463   0.121206    0.102686\n",
              "2  252  2020-10-01  2020-12-31       DDPG   0.157398   0.202503    0.205319\n",
              "3  315  2020-12-31  2021-04-05        PPO    0.18216   0.297779    0.175079"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4JKB--8tj1g"
      },
      "source": [
        "unique_trade_date = processed[(processed.date > val_test_start)&(processed.date <= val_test_end)].date.unique()"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9mKF7GGtj1g",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1fdc662-bcb5-41f9-875c-25eb4ffa4aee"
      },
      "source": [
        "df_trade_date = pd.DataFrame({'datadate':unique_trade_date})\n",
        "\n",
        "df_account_value=pd.DataFrame()\n",
        "for i in range(rebalance_window+validation_window, len(unique_trade_date)+1,rebalance_window):\n",
        "    temp = pd.read_csv('results/account_value_trade_{}_{}.csv'.format('ensemble',i))\n",
        "    df_account_value = df_account_value.append(temp,ignore_index=True)\n",
        "sharpe=(252**0.5)*df_account_value.account_value.pct_change(1).mean()/df_account_value.account_value.pct_change(1).std()\n",
        "print('Sharpe Ratio: ',sharpe)\n",
        "df_account_value=df_account_value.join(df_trade_date[validation_window:].reset_index(drop=True))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sharpe Ratio:  1.5454149316420212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyosyW7_tj1g",
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          "height": 206
        },
        "outputId": "c6c345d3-165f-4aac-c26d-098b03833038"
      },
      "source": [
        "df_account_value.head()"
      ],
      "execution_count": 25,
      "outputs": [
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   account_value        date  daily_return    datadate\n",
              "0   1.000000e+06  2020-07-02           NaN  2020-07-02\n",
              "1   1.004361e+06  2020-07-06      0.004361  2020-07-06\n",
              "2   9.957478e+05  2020-07-07     -0.008576  2020-07-07\n",
              "3   1.000690e+06  2020-07-08      0.004964  2020-07-08\n",
              "4   9.875919e+05  2020-07-09     -0.013090  2020-07-09"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLsRdw2Ctj1h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "9a7ddcbd-5fd9-4fe3-f1f2-7fb8c182ac4b"
      },
      "source": [
        "%matplotlib inline\n",
        "df_account_value.account_value.plot()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fca475999d0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "scrolled": true,
        "outputId": "76140e45-d478-4736-defb-1355fc80c06e"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.228941\n",
            "Cumulative returns     0.228941\n",
            "Annual volatility      0.140345\n",
            "Sharpe ratio           1.545415\n",
            "Calmar ratio           2.432010\n",
            "Stability              0.898921\n",
            "Max drawdown          -0.094137\n",
            "Omega ratio            1.298442\n",
            "Sortino ratio          2.237300\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.065696\n",
            "Daily value at risk   -0.016821\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiHhM1YkoCel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ab9a34c-149e-4e1f-8069-b6c8b73435e8"
      },
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = df_account_value.loc[0,'date'],\n",
        "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 8)\n",
            "Annual return          0.337432\n",
            "Cumulative returns     0.335890\n",
            "Annual volatility      0.146098\n",
            "Sharpe ratio           2.072078\n",
            "Calmar ratio           3.778308\n",
            "Stability              0.944970\n",
            "Max drawdown          -0.089308\n",
            "Omega ratio            1.411593\n",
            "Sortino ratio          3.102218\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.078766\n",
            "Daily value at risk   -0.017205\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HggausPRoCem",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8c1889e3-33da-46a2-efbc-54da27ed441c"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = df_account_value.loc[0,'date'],\n",
        "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Compare to DJIA===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 8)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-07-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>12</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>22.894%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>22.894%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>14.035%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>2.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-9.414%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>2.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-1.682%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>-0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.41</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-10-30</td>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.88</td>\n",
              "      <td>2021-05-10</td>\n",
              "      <td>2021-06-18</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.71</td>\n",
              "      <td>2021-02-08</td>\n",
              "      <td>2021-02-26</td>\n",
              "      <td>2021-03-10</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.47</td>\n",
              "      <td>2020-07-22</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>2020-08-10</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.04</td>\n",
              "      <td>2021-01-26</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-05</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkdWMUWKoCem"
      },
      "source": [
        ""
      ],
      "execution_count": 29,
      "outputs": []
    }
  ]
}